Image processing program, image processing apparatus, and image processing method

ABSTRACT

An image processing method is performed by a computer, for determining that a line is a crack or something other than the crack. The method includes: extracting a linear region from an image of an object captured by an imaging apparatus; determining a luminosity change in a direction traversing the linear region at each of a plurality of positions separate in a longitudinal direction of the linear region; and identifying a type of the linear region based on the luminosity changes at the plurality of positions.

CROSS-REFERENCE TO RELATED APPLICATION

This application is based upon and claims the benefit of priority of theprior Japanese Patent Application No. 2018-112612, filed on Jun. 13,2018, the entire contents of which are incorporated herein by reference.

FIELD

The embodiment discussed herein is related to an image processingprogram, an image processing apparatus, and an image processing method.

BACKGROUND

A crack detection device configured to detect presence or absence of acrack in a concrete wall smeared with soot or the like, and a surfacedefect detection device configured to detect a surface defect such as acrack or a scratch in an inspection target surface that exhibitsmultiple colors are known (for example, see Japanese Laid-open PatentPublication No. 2014-6219 and Japanese Laid-open Patent Publication No.58-39936).

The crack detection device in Japanese Laid-open Patent Publication No.2014-6219 radiates at least one of red light, blue light, and greenlight toward an inspection target object, and takes a picture of theinspection target object. Then, the crack detection device calculateschroma of each pixel of the obtained image from the RGB values, anddetermines presence or absence of a crack in a surface of the inspectiontarget object.

However, the above technique may falsely recognize hair, a black anddirty spider web, a black line drawn with a black pen, or the likepresent on a concrete wall, as a crack.

The above problem occurs not only in a case of detecting a crack fromthe image obtained by taking a picture of the concrete wall but also ina case of identifying a type of a linear region contained in an imageobtained by taking a picture of another object.

In one aspect, it is an object of the embodiment to precisely identify atype of a linear region contained in an image obtained by taking apicture of an object.

SUMMARY

According to an aspect of the embodiments, an image processing method isperformed by a computer, for determining that a line is a crack orsomething other than the crack. The method includes: extracting a linearregion from an image of an object captured by an imaging apparatus;determining a luminosity change in a direction traversing the linearregion at each of a plurality of positions separate in a longitudinaldirection of the linear region; and identifying a type of the linearregion based on the luminosity changes at the plurality of positions.

The object and advantages of the invention will be realized and attainedby means of the elements and combinations particularly pointed out inthe claims.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory and arenot restrictive of the invention.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a functional configuration diagram of an image processingapparatus;

FIG. 2 is an image processing flowchart;

FIG. 3 is a functional configuration diagram of an image processingsystem;

FIG. 4 is a diagram illustrating an image-capturing target object;

FIGS. 5A and 5B are diagrams illustrating light radiation angles;

FIGS. 6A and 6B are diagrams illustrating vector data;

FIGS. 7A and 7B are diagrams illustrating calculation target positions;

FIG. 8 is a diagram illustrating vector data including a branch;

FIG. 9 is a diagram illustrating a calculation target region;

FIGS. 10A to 10F are graphs illustrating luminosity information;

FIG. 11 is a flowchart illustrating a specific example of imageprocessing;

FIGS. 12A and 12B are tables illustrating types and statistics of blackline regions registered in a database;

FIGS. 13A and 13B are tables illustrating statistics of luminositychanges indicated by luminosity information; and

FIG. 14 is a configuration diagram of an information processingapparatus.

DESCRIPTION OF EMBODIMENTS

Hereinafter, an embodiment will be described in detail with reference tothe drawings.

FIG. 1 illustrates a functional configuration example of an imageprocessing apparatus of the embodiment. An image processing apparatus101 in FIG. 1 includes a storage unit 111, an extraction unit 112, andan identifying unit 113. The storage unit 111 stores an image 121 of anobject captured by an imaging apparatus. The extraction unit 112 and theidentifying unit 113 perform image processing on the image 121.

FIG. 2 is a flowchart illustrating an example of the image processingperformed by the image processing apparatus 101 in FIG. 1. First, theextraction unit 112 extracts a linear region from the image 121 (step201). Next, at each of a plurality of positions separate in a lengthwisedirection of the linear region having been extracted by the extractionunit 112, the identifying unit 113 determines a luminosity change in adirection traversing the linear region (step 202). Then, the identifyingunit 113 identifies a type of the linear region based on the luminositychanges at the plurality of positions (step 203).

According to the image processing apparatus 101 in FIG. 1, the type of alinear region contained in the image obtained by taking a picture of theobject may be precisely identified.

FIG. 3 illustrates a functional configuration example of an imageprocessing system including the image processing apparatus 101 inFIG. 1. The image processing system in FIG. 3 includes an imageprocessing apparatus 301, an imaging apparatus 302, and a server 303.

The image processing apparatus 301 corresponds to the image processingapparatus 101 in FIG. 1, and includes a storage unit 311, an acquisitionunit 312, an extraction unit 313, an identifying unit 314, acommunication unit 315, and an output unit 316. The server 303 includesa database 331. The storage unit 311, the extraction unit 313, and theidentifying unit 314 correspond to the storage unit 111, the extractionunit 112, and the identifying unit 113 in FIG. 1, respectively, and animage 321 corresponds to the image 121 in FIG. 1.

The imaging apparatus 302 is, for example, a camera including elementsfor image capturing such as a charge-coupled device (CCD) and acomplementary metal-oxide-semiconductor (CMOS), and captures the image321 of an object by taking a picture of the object irradiated withlight. Then, the imaging apparatus 302 outputs the image 321 of theobject to the image processing apparatus 301.

The acquisition unit 312 of the image processing apparatus 301 acquiresthe image 321 from the imaging apparatus 302 and stores it in thestorage unit 311. The extraction unit 313 extracts a linear region fromthe image 321, converts the extracted linear region into vector data322, and stores the vector data in the storage unit 311. The vector data322 includes a plurality of line segments approximating the linearregion, and each line segment is represented by two-dimensionalcoordinates of endpoints of both ends thereof.

The identifying unit 314 determines, using the vector data 322, aplurality of positions separate in the lengthwise direction of thelinear region as luminosity change calculation targets. Then, theidentifying unit 314 calculates a luminosity change in the directiontraversing the linear region at each position, and stores luminosityinformation 323 indicating the calculated luminosity change in thestorage unit 311.

In the database 331 of the server 303, statistics that indicate theluminosity changes having been calculated by the image processingapparatus 301 are registered, where the luminosity changes arerespectively associated with a plurality of types of linear regionscontained in the images of the object having been captured in the past.

The communication unit 315 communicates with the server 303 via acommunication network to acquire the types of linear regions and thestatistics associated with the types thereof from the database 331. Theidentifying unit 314 identifies the type of the linear region extractedfrom the image 321 using the luminosity information 323 stored in thestorage unit 311 and the types and statistics acquired from the database331. The output unit 316 outputs the type of the linear regionidentified by the identifying unit 314.

FIG. 4 illustrates an example of an image-capturing target object. Alight source 402 radiates light 412 of a plurality of colors includingred, blue, green, and the like toward a concrete wall 401, and theimaging apparatus 302 takes a picture of the concrete wall 401irradiated with the light.

In this case, a linear region contained in the image obtained by takinga picture of the concrete wall 401 is, for example, a black line regionwhere a crack 411, hair, a black and dirty spider web, a black linedrawn with a black pen, or the like is seen. The identifying unit 314identifies the type of the black line region extracted from the image asthe crack 411 or other types than the crack 411. For example, a cameracapable of image capturing of the crack 411 with a width being down toabout 0.1 mm may be used, as the imaging apparatus 302.

For example, blue light is likely to be scattered in the air since itswavelength is short, and red light is unlikely to be scattered in theair since its wavelength is long. Accordingly, in a black line regionother than the crack 411, luminosity distribution is significantlydifferent between an image obtained by taking a picture of the concretewall 401 being irradiated with blue light and an image obtained bytaking a picture of the concrete wall 401 being irradiated with redlight.

In contrast, in the case of the crack 411, since the reflective lightbecomes weak regardless of the wavelength of light, the luminositydistribution is hardly different in the black line region between whenblue light is radiated and when red light is radiated. Therefore, byusing images obtained by taking pictures of the object toward whichlight of a plurality of colors is radiated, it is possible to identifywhether or not the type of the black line region is the crack 411.

Note that the image-capturing target object is not limited to theconcrete wall 401, and may be an object of different material such asglass, ceramic, plastic, wood, or the like. The linear region containedin the object image is not limited to the black line region where acrack or the like is seen, and may be a region where a repairingmaterial filled in a crack, a rope, a white line drawn with a white pen,or the like is seen.

FIGS. 5A and 5B illustrate examples of radiation angles of lightradiated by the light source 402. An x-axis is included in a frontsurface of the concrete wall 401, and a y-axis represents a directionperpendicular to the front surface of the concrete wall 401. The imagingapparatus 302 is disposed on the y-axis and takes a picture of the frontsurface of the concrete wall 401.

FIG. 5A illustrates an example in which the light source 402 radiateslight toward the front surface of the concrete wall 401 at a smallangle. As an angle α between the front surface of the concrete wall 401and the light radiation angle, an angle in a range from 0 to 30 degrees,for example, is used.

FIG. 5B illustrates an example in which the light source 402 radiateslight toward the front surface of the concrete wall 401 at a largeangle. As an angle β between the front surface of the concrete wall 401and the light radiation angle, an angle in a range from 60 to 90degrees, for example, is used.

The light source 402 radiates the light 412 toward the concrete wall 401at a plurality of angles, and the imaging apparatus 302 takes picturesof the concrete wall 401 irradiated with the light. For example, in acase where a three-dimensional object such as hair or a spider web ispresent on the concrete wall 401, as the radiation angle of the light412 is smaller, the shadow thereof is likely to be cast and the blackline region is likely to be spread. On the other hand, as the radiationangle of the light 412 is larger, the shadow is unlikely to be cast andthe black line region is unlikely to be spread. Therefore, by usingimages obtained by taking pictures of the object toward which light isradiated at a plurality of angles, it is possible to identify whether ornot the type of the black line region is a three-dimensional object.

Each of FIGS. 6A and 6B illustrates an example of the vector data 322representing a black line region of the crack 411. FIG. 6A illustratesan example of vector data in the form of a polygonal line. Vector data601 to vector data 604 represent four black line regions bending along acrack in the concrete wall. FIG. 6B illustrates an example of vectordata in the form of a straight line. Vector data 605 represents oneblack line region extending along a crack in the concrete wall. Each ofthe vector data 601 to vector data 605 is divided into line segments ata predetermined interval.

Each of FIGS. 7A and 7B illustrates an example of calculation targetpositions on the vector data 322. FIG. 7A illustrates an example ofcalculation target positions on the vector data 601 to vector data 604in FIG. 6A, and FIG. 7B illustrates an example of calculation targetpositions on the vector data 605 in FIG. 6B.

Each of calculation target lines 701 in FIGS. 7A and 7B indicates aposition where a luminosity change in a direction traversing the blackline region of each of the vector data 601 to vector data 605 iscalculated. In the case where the length of the black line region isless than a predetermined value, the calculation target lines 701 may beset in three locations, that is, at both ends and the center of theblack line region.

FIG. 8 illustrates an example of the vector data 322 including a branch.In the case where the vector data branches, vector data beyond abranching point 801 is also divided into line segments at apredetermined interval for each branch.

FIG. 9 illustrates an example of a calculation target region at acalculation target position. A calculation target region 903 is set in adirection traversing a black line region 901 at a calculation targetposition 902 of the black line region 901. The calculation target region903 may be set in a direction orthogonal to the line segment of thevector data at the calculation target position 902. It is desirable forthe length of the calculation target region 903 to be three times ormore of the width of the black line region 901. In the example of FIG.9, the width of the black line region 901 is configured of three pixels,and the length of the calculation target region 903 is configured of 15pixels.

FIGS. 10A to 10F illustrate examples of the luminosity information 323indicating luminosity changes in the calculation target regions. Ahorizontal axis represents positions of pixels included in thecalculation target region traversing the black line region, and alongitudinal axis represents luminosity of the pixels at the respectivepositions.

FIGS. 10A to 10C illustrate luminosity changes of an image B capturedwhile blue light being radiated toward the concrete wall at a largeangel as illustrated in FIG. 5B. FIGS. 10D to 10F illustrate luminositychanges of an image R captured while red light being radiated toward theconcrete wall at a small angel as illustrated in FIG. 5A.

Graphs 1001-1 to 1003-1 in FIG. 10A and graphs 1011-1 to 1013-1 in FIG.10D indicate the luminosity changes at the first calculation targetposition among the calculation target positions set in a total of threelocations. Graphs 1001-2 to 1003-2 in FIG. 10B and graphs 1011-2 to1013-2 in FIG. 10E indicate the luminosity changes at the secondcalculation target position. Graphs 1001-3 to 1003-3 in FIG. 10C andgraphs 1011-3 to 1013-3 in FIG. 10F indicate the luminosity changes atthe third calculation target position.

The graph 1001-i and the graph 1011-i (where i=1 to 3) indicate theluminosity changes at the i-th calculation target position in a casewhere the type of the black line region is a crack. The graph 1002-i andthe graph 1012-i indicate the luminosity changes at the i-th calculationtarget position in a case where the type of the black line region is ablack line drawn with a black pen. The graph 1003-i and the graph 1013-iindicate the luminosity changes at the i-th calculation target positionin a case where the type of the black line region is hair.

In the case of the image R, since red light having a long wavelength isradiated, the luminosity of pixels where a crack with a width of equalto or smaller than approximately 0.2 mm is seen becomes substantiallyequal to the luminosity of pixels where a black pen line or hair isseen, due to the reflective light from the concrete in the vicinity ofthe crack. On the other hand, in the case of the image B, since bluelight having a short wavelength is radiated, the luminosity of pixelswhere the crack is seen becomes smaller than the luminosity of pixelswhere the black pen line or the hair is seen.

In the case of a crack, since the luminosity is smallest at the deepestportion, the luminosity change in a width direction of the crack hasonly a minimum value and does not have any extreme value (a maximalvalue or a minimal value) other than the minimum value. On the otherhand, in the case of a black pen or hair, the luminosity variouslychanges depending on ink fading or unevenness of the hair, and theluminosity change tends to have an extreme value other than a minimumvalue. For example, the luminosity has a maximal value in an ink fadingportion of the black pen or in a portion where the reflective light fromthe hair is intensive.

Accordingly, in a case where the luminosity change does not have anyextreme value other than the minimum value at any of the calculationtarget positions set in the three locations for the image R and theimage B, it is highly possible that the black line region is a crack. Inthe case of hair, a portion where the luminosity is small (a darkportion) is likely to be spread due to a shadow thereof being cast.

For example, since the graphs 1002-1 to 1002-3, the graph 1012-2, andthe graph 1012-3 have maximal values, the type of the black line regioncorresponding to these graphs may be determined to be a type other thana crack. Likewise, since the graphs 1013-1 to 1013-3 also have maximalvalues, the type of the black line region corresponding to these graphsmay also be determined to be a type other than a crack.

In addition, since a portion where the luminosity is small in the graph1013-i is more spread than a portion where the luminosity is small inthe graph 1003-i, the type of the black line region corresponding tothese graphs may be determined to be a type other than a crack.

The number of locations in which the calculation target positions areset is not limited to three; that is, the type of a black line regionmay be determined by using luminosity changes at a plurality ofcalculation target positions set in two or more locations. As the numberof calculation target positions is larger, the precision of thedetermination result is improved.

FIG. 11 is a flowchart illustrating a specific example of imageprocessing performed by the image processing system in FIG. 3. First,the light source 402 radiates light of a plurality of colors toward theconcrete wall 401 at a small angle, and the imaging apparatus 302captures the images 321 of the concrete wall 401 irradiated with thelight of the plurality of colors without using the flash (step 1101).

Next, the light source 402 radiates light of a plurality of colorstoward the concrete wall 401 at a large angle, and the imaging apparatus302 captures the image 321 of the concrete wall 401 irradiated with thelight of the plurality of colors without using the flash (step 1102).

Subsequently, the extraction unit 313 extracts a black linear regioncontained in each of a plurality of captured images 321 as a black lineregion (step 1103). Then, the identifying unit 314 checks whether or nota shadow is contained in the extracted black line regions by comparingthe widths of the black line regions extracted from the plurality ofimages 321 with each other (step 1104).

For example, the identifying unit 314 superposes a black line region ofthe image R and a black line region of the image B, and determines thata shadow is not contained in these black line regions when the widths ofboth the black line regions match each other. When the width of one ofthe black line regions is thicker than the width of the other one of theblack line regions, the identifying unit 314 determines that a shadow iscontained in the thicker black line region. Thus, based on presence orabsence of a shadow, it is possible to identify whether or not the typeof the black line region is a three-dimensional object.

In a case where a shadow is contained in the black line region (step1104, YES), the identifying unit 314 determines the type of the blackline region as a type other than a crack (step 1110), and the outputunit 316 outputs the determined type of the black line region.

On the other hand, in a case where a shadow is not contained in theblack line region (step 1104, NO), the extraction unit 313 converts theblack line region into the vector data 322 (step 1105).

Next, the identifying unit 314 determines a plurality of calculationtarget positions on the vector data 322 of each image 321, and sets acalculation target region at each calculation target position.Subsequently, the identifying unit 314 generates the luminosityinformation 323 indicating a luminosity change in each of thecalculation target regions (step 1106), and checks whether or not theluminosity change includes an extreme value other than the minimum value(step 1107).

In a case where the luminosity change includes an extreme value otherthan the minimum value (step 1107, YES), the identifying unit 314determines the type of the black line region as a type other than acrack (step 1110), and the output unit 316 outputs the determined typeof the black line region.

On the other hand, in a case where the luminosity change does notinclude an extreme value other than the minimum value (step 1107, NO),the identifying unit 314 acquires the type and statistics of the blackline region from the database 331 via the communication unit 315.Subsequently, the identifying unit 314 determines whether or not theluminosity change indicated by the luminosity information 323corresponds to a crack, using the type and statistics acquired from thedatabase 331 (step 1108).

For example, the identifying unit 314 calculates statistics of theluminosity change indicated by the luminosity information 323, andcompares the calculated statistics with the statistics acquired from thedatabase 331, thereby determining whether or not the luminosity changecorresponds to a crack. As the statistics of the luminosity change, amaximum value, a minimum value, a median value, a mean value, and thelike of the luminosity in the calculation target region may be used.

In a case where the luminosity change corresponds to a crack (step 1108,YES), the identifying unit 314 determines the type of the black lineregion as a crack (step 1109), and the output unit 316 outputs thedetermined type of the black line region. On the other hand, in a casewhere the luminosity change does not correspond to a crack (step 1108,NO), the identifying unit 314 determines the type of the black lineregion as a type other than a crack (step 1110), and the output unit 316outputs the determined type of the black line region.

FIGS. 12A and 12B each illustrate an example of types and statistics ofblack line regions registered in the database 331. Note that “crack”,“black pen”, and “hair” indicate types of a black line region, and “P1”to “P5” indicate calculation target positions on the black line region.Each numeral in a column indicated by the calculation target position Pj(where j=1 to 5) represents a minimum value of the luminosity change atthe calculation target position Pj on the black line region of the typeindicated by the corresponding line. Each numeral in the columnindicated by “mean value” represents a mean value of five minimum valuesat the calculation target positions P1 to P5.

FIG. 12A illustrates an example of minimum values and mean valuescalculated from images captured while blue light being radiated toward aconcrete wall at a large angel. FIG. 12B illustrates an example ofminimum values and mean values calculated from images captured while redlight being radiated toward a concrete wall at a small angel.

In a case of using any condition of FIG. 12A or FIG. 12B, minimum valuesand mean values of the luminosity differ among the crack, the black pen,and the hair. Accordingly, in a case where a picture of another concretewall is taken under the same condition, minimum values and mean valuesof luminosity may take similar values as long as the type of the blackline region is the same.

FIGS. 13A and 13B each illustrate an example of statistics of luminositychanges indicated by the luminosity information 323. “P11” to “P15”represent calculation target positions on a black line region. A numeralat each of the calculation target positions represents a minimum valueof the luminosity change at each of the calculation target positions,and “mean value” represents a mean value of five minimum values at thecalculation target positions P11 to P15.

FIG. 13A illustrates an example of minimum values and a mean valuecalculated from an image captured while blue light being radiated towarda concrete wall at a large angel. FIG. 13B illustrates an example ofminimum values and a mean value calculated from an image captured whilered light being radiated toward a concrete wall at a small angel.

For example, when a mean value of any of the types in FIG. 12A or 12B istaken as “A”, in a case where a mean value in FIG. 13A or 13B fallswithin a range from A−Δ to A+Δ, the identifying unit 314 may determinethat the luminosity change indicated by the luminosity information 323corresponds to the above-mentioned type.

When Δ=1, a range R1 of the mean value corresponding to the crack inFIG. 12A is 36.2 to 38.2, a range R2 of the mean value corresponding tothe black pen in FIG. 12A is 39.2 to 41.2, and a range R3 of the meanvalue corresponding to the hair in FIG. 12A is 41 to 43. Further, arange R4 of the mean value corresponding to the crack in FIG. 12B is61.4 to 63.4, a range R5 of the mean value corresponding to the blackpen in FIG. 12B is 64.2 to 66.2, and a range R6 of the mean valuecorresponding to the hair in FIG. 12B is 68.2 to 70.2.

The mean value 41.2 in FIG. 13A is not included in the range R1 of thecrack in FIG. 12A, but is included in the range R2 of the black pen andthe range R3 of the hair in FIG. 12A. Accordingly, it may be determinedthat the type of the black line region in FIG. 13A is either the blackpen or the hair. Further, the mean value 68.6 in FIG. 13B is notincluded in any of the range R4 of the crack and the range R5 of theblack pen in FIG. 12B, but is included in the range R6 of the hair inFIG. 12B. Accordingly, the type of the black line region in FIG. 13B isdetermined to be hair.

As discussed above, by comparing the statistics of the luminosity changeindicated by the luminosity information 323 with the statistics acquiredfrom the database 331, it is possible to improve the precision ofdetermination on the type of the black line region. As the number ofimages captured under different conditions in colors of light or anglesof light radiated toward the same concrete wall is increased, theprecision of the determination is improved.

The identifying unit 314 may register the calculated statistics of theluminosity changes in the database 331 in which the calculatedstatistics are associated with the type of the black line region havingbeen confirmed by an operator at the inspection site, and may use theregistered statistics in the next image processing.

According to the image processing in FIG. 11, since the type of theblack line region contained in the image of the concrete wall isautomatically identified, the inspection of the concrete wall isefficiently carried out by the operator. For example, even if hair, ablack and dirty spider web, a black line drawn with a black pen, or thelike is present on the concrete wall, such matter may be distinguishedfrom a crack.

Note that, in step 1104, instead of checking the presence or absence ofa shadow by comparing the widths of the black line regions of theplurality of images 321, it is also possible to check the presence orabsence of a shadow by comparing the luminosity changes at the samecalculation target position of the plurality of images 321. In thiscase, the identifying unit 314 determines a width of a region having asmaller luminosity than a predetermined value from the luminositychanges at the same calculation target position of the plurality ofimages 321. Then, in a case where a difference in width between theregions of two images 321 is greater than a threshold, the identifyingunit 314 determines that a shadow is contained in the region having awider width.

The configuration of the image processing apparatus 101 in FIG. 1 ismerely an example, and part of the constituent elements may be omittedor modified in accordance with usage or conditions of the imageprocessing apparatus 101.

The configuration of the image processing system in FIG. 3 is merely anexample, and part of the constituent elements may be omitted or modifiedin accordance with usage or conditions of the image processing apparatus301. For example, in a case where the image 321 is stored beforehand inthe storage unit 311, the acquisition unit 312 may be omitted, and in acase where the database 331 is not used, the communication unit 315 maybe omitted. In a case where it is not required to output the type of alinear region, the output unit 316 may be omitted.

The flowcharts in FIGS. 2 and 11 are merely examples, and part of theprocessing may be omitted or modified in accordance with theconfiguration or conditions of the image processing apparatus. Forexample, in the image processing of FIG. 11, in the case where the image321 is stored beforehand in the storage unit 311, the processing in step1101 and the processing in step 1102 may be omitted. In a case where itis not required to check the presence or absence of a shadow, theprocessing in step 1104 may be omitted, and in the case where thedatabase 331 is not used, the processing in step 1108 may be omitted.

The concrete wall 401 illustrated in FIG. 4 is merely an example, andthe image-capturing target object may be an object made of anothermaterial. The light radiation angles illustrated in FIGS. 5A and 5B aremerely examples, and other radiation angles may be employed inaccordance with the configuration or conditions of the image processingapparatus. The black line regions, vector data, and calculation targetlines illustrated in FIG. 6A to FIG. 8 are merely examples, and theblack line regions, vector data, and calculation target lines vary inaccordance with the image 321.

The calculation target region 903 illustrated in FIG. 9 is merely anexample, and a calculation target region of another shape or anothersize may be employed in accordance with the configuration or conditionsof the image processing apparatus. The calculation target region 903 maynot be orthogonal to a line segment of vector data. The luminosityinformation 323 illustrated in FIGS. 10A to 10F is exemplary, and theluminosity information 323 varies in accordance with the image 321.

The minimum values and mean values illustrated in FIG. 12A to FIG. 13Bare merely examples, and the statistics of luminosity changes vary inaccordance with the luminosity changes at the respective calculationtarget positions.

FIG. 14 illustrates a configuration example of an information processingapparatus (computer) used as the image processing apparatus 101 in FIG.1 and the image processing apparatus 301 in FIG. 3. The informationprocessing apparatus in FIG. 14 includes a central processing unit (CPU)1401, a memory 1402, an input device 1403, an output device 1404, anauxiliary storage device 1405, a medium driving device 1406, and anetwork connection device 1407. These constituent elements are connectedto one another through a bus 1408. The imaging apparatus 302 in FIG. 3may be connected to the bus 1408.

The memory 1402 is, for example, a semiconductor memory such as a readonly memory (ROM), a random access memory (RAM) or a flash memory, andstores therein programs and data used in the processing. The memory 1402may be used as the storage unit 111 in FIG. 1 and the storage unit 311in FIG. 3.

The CPU 1401 (processor) operates as the extraction unit 112 and theidentifying unit 113 in FIG. 1 by executing a program while using thememory 1402. The CPU 1401 also operates as the acquisition unit 312, theextraction unit 313, and the identifying unit 314 in FIG. 3 by executingthe program while using the memory 1402.

The input device 1403 is, for example, a keyboard or a pointing device,and is used for inputting a command or information from an operator or auser. The output device 1404 is, for example, a display device, aprinter, a speaker, or the like and is used for outputting an inquiry ora command, and a result of processing to be given to the operator oruser. The output device 1404 may be used as the output unit 316 in FIG.3. The result of processing may be information indicating types oflinear regions.

The auxiliary storage device 1405 is, for example, a magnetic diskdrive, an optical disk drive, a magneto-optical disk drive, a tapedrive, or the like. The auxiliary storage device 1405 may be a hard diskdrive or a flash memory. The information processing apparatus may storeprograms and data in the auxiliary storage device 1405, and use theseprograms and data by loading them on the memory 1402. The auxiliarystorage device 1405 may be used as the storage unit 111 in FIG. 1 andthe storage unit 311 in FIG. 3.

The medium driving device 1406 drives a portable recording medium 1409,and accesses its recording contents. The portable recording medium 1409is a memory device, a flexible disk, an optical disk, a magneto-opticaldisk, or the like. The portable recording medium 1409 may be a compactdisk read only memory (CD-ROM), a digital versatile disk (DVD), aUniversal Serial Bus (USB) memory, or the like. An operator or a usermay beforehand store programs and data in the portable recording medium1409, and use these programs and data by loading them on the memory1402.

As described above, a computer-readable recording medium configured tostore programs and data used in the processing is a physical(non-transitory) recording medium like the memory 1402, the auxiliarystorage device 1405, or the portable recording medium 1409.

The network connection device 1407 is connected to a communicationnetwork such as a local area network (LAN) or a wide area network (WAN),and is a communication interface circuit configured to perform dataconversion required for the communication. The information processingapparatus may receive programs and data from external devices via thenetwork connection device 1407, and load these programs and data on thememory 1402 to use them. The network connection device 1407 may be usedas the communication unit 315 in FIG. 3.

Not all of the constituent elements in FIG. 14 are required to beincluded in the information processing apparatus, and part of theconstituent elements may be omitted in accordance with the usage orconditions of the apparatus. For example, in a case where it is notrequired to interact with an operator or a user, the input device 1403and the output device 1404 may be omitted. In a case where the portablerecording medium 1409 or the communication network is not used, themedium driving device 1406 or the network connection device 1407 may beomitted.

As the server 303 in FIG. 3, the same information processing apparatusas that in FIG. 14 may be used. In this case, the auxiliary storagedevice 1405 is used as the database 331.

The disclosed embodiment and its advantages have been described indetail thus far, and it would be possible for those skilled in the artto carry out various kinds of modification, addition, and omissionwithout departing from the spirit and scope of the embodiment as setforth clearly in the appended claims.

All examples and conditional language provided herein are intended forthe pedagogical purposes of aiding the reader in understanding theinvention and the concepts contributed by the inventor to further theart, and are not to be construed as limitations to such specificallyrecited examples and conditions, nor does the organization of suchexamples in the specification relate to a showing of the superiority andinferiority of the invention. Although one or more embodiments of thepresent invention have been described in detail, it should be understoodthat the various changes, substitutions, and alterations could be madehereto without departing from the spirit and scope of the invention.

What is claimed is:
 1. A non-transitory computer-readable storage mediumhaving stored therein a program for causing a computer to execute aprocess for image processing, the process comprising: extracting alinear region from each of a plurality of images of an object capturedby an imaging apparatus, wherein the plurality of images are capturedwhile light of different colors is respectively radiated toward theobject; determining a luminosity change in a direction traversing thelinear region at each of a plurality of positions separate in alongitudinal direction of the linear region of each of the plurality ofimages of the different colors; and identifying a type of the linearregion based on a comparison between the luminosity changes of thedifferent colors at each of the plurality of positions of the linearregions extracted from the plurality of images.
 2. The non-transitorycomputer-readable storage medium according to claim 1, wherein in theidentifying, the type of the linear region is identified based onwhether or not an extreme value other than a minimum value of luminosityis present in any of the luminosity changes at the plurality ofpositions.
 3. The non-transitory computer-readable storage mediumaccording to claim 1, wherein in the extracting, extracted is a linearregion from each of a plurality of images captured while light beingradiated respectively at a plurality of angles toward the object; in thedetermining of the luminosity change, determined is a luminosity changein a direction traversing each of the linear regions with respect to thelinear regions of the plurality of images; and in the identifying, thetype of the linear region is identified based on luminosity changes at aplurality of positions of the linear regions extracted from theplurality of images.
 4. The non-transitory computer-readable storagemedium according to claim 3, wherein in the identifying, the type of thelinear region is identified based on a comparison result obtained bycomparing widths of the linear regions extracted from the plurality ofimages.
 5. The non-transitory computer-readable storage medium accordingto claim 3, wherein in the identifying, the type of the linear region isidentified based on a comparison result obtained by comparing luminositychanges at a same position of the linear regions extracted from theplurality of images.
 6. The non-transitory computer-readable storagemedium according to claim 1, the process further comprising: processingin which first statistics indicating luminosity changes at a pluralityof positions of linear regions and registered being associated with aplurality of types are acquired, the acquired first statistics arecompared with second statistics indicating luminosity changes at aplurality of positions of the linear regions extracted from theplurality of images, and the type of the linear region is determinedbased on a comparison result obtained by comparing the first statisticsand the second statistics.
 7. The non-transitory computer-readablestorage medium according to claim 1, wherein the object is a concretewall, and the types of the linear regions are a crack and a type otherthan the crack.
 8. An image processing apparatus comprising: a memorystoring an image of an object captured by an imaging apparatus; and aprocessor coupled to the memory and configured to execute a processincluding: extracting a linear region from each of a plurality of imagesof an object captured by an imaging apparatus, wherein the plurality ofimages are captured while light of different colors is respectivelyradiated toward the object; determining a luminosity change in adirection traversing the linear region at each of a plurality ofpositions separate in a longitudinal direction of the linear region ofeach of the plurality of images of the different colors; and identifyinga type of the linear region based on a comparison between the luminositychanges of the different colors at each of the plurality of positions ofthe linear regions extracted from the plurality of images.
 9. The imageprocessing apparatus according to claim 8, wherein in the identifying,the type of the linear region is identified based on whether or not anextreme value other than a minimum value of luminosity is present in anyof the luminosity changes at the plurality of positions.
 10. The imageprocessing apparatus according to claim 8, wherein in the extracting,extracted is a linear region from each of a plurality of images capturedwhile light being radiated respectively at a plurality of angles towardthe object; in the determining of the luminosity change, determined is aluminosity change in a direction traversing each of the linear regionswith respect to the linear regions of the plurality of images; and inthe identifying, the type of the linear region is identified based onluminosity changes at a plurality of positions of the linear regionsextracted from the plurality of images.
 11. An image processing methodperformed by a computer, the method comprising: extracting a linearregion from each of a plurality of images of an object captured by animaging apparatus, wherein the plurality of images are captured whilelight of different colors is respectively radiated toward the object;determining a luminosity change in a direction traversing the linearregion at each of a plurality of positions separate in a longitudinaldirection of the linear region of each of the plurality of images of thedifferent colors; and identifying a type of the linear region based on acomparison between the luminosity changes of the different colors ateach of the plurality of positions of the linear regions extracted fromthe plurality of images.
 12. The image processing method according toclaim 11, wherein in the identifying, the type of the linear region isidentified based on whether or not an extreme value other than a minimumvalue of luminosity is present in any of the luminosity changes at theplurality of positions.